ArticleScientific reports2021
A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Development of a real-world, therapeutic drug monitoring-informed model to predict teicoplanin daily dose in pediatric intensive care unit patients with bacterial infections.International journal of clinical pharmacy · 2026Article
- Predicting Ketamine Exposure in Pediatric Status Epilepticus: A Pharmacokinetic-Machine Learning Approach.European journal of drug metabolism and pharmacokinetics · 2026Article
- A Comparison of AI and Population PK Models to Predict the Concentrations of Antiepileptic Drugs Using Therapeutic Drug Monitoring Records.Clinical and translational science · 2025Article
- A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring.Nature biomedical engineering · 2025Review
- Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques.Pharmaceutical research · 2025Article
- Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Article
- Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare.Pharmaceutics · 2024Review
- The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review.Sensors (Basel, Switzerland) · 2023Review
- How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors.Biomedicines · 2023Article
- Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population.Therapeutic advances in drug safety · 2023Article
- Construction and Interpretation of Prediction Model of Teicoplanin Trough ConcentrationFrontiers in medicine · 2022Article
- An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine.Frontiers in pharmacology · 2022Article
- Machine learning advances the integration of covariates in population pharmacokinetic models: Valproic acid as an example.Frontiers in pharmacology · 2022Article
- Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?Frontiers in molecular biosciences · 2021Article
- Case Report: Predicting the Range of Lamotrigine Concentration Using Pharmacokinetic Models Based on Monte Carlo Simulation: A Case Study of Antiepileptic Drug-Related Leukopenia.Frontiers in pharmacology · 2021Article
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learning (ML) algorithms. A total of 1141 therapeutic drug-monitoring measurements were used, 80% of which were randomly selected as the "derivation cohort" to develop the prediction algorithm, and the remaining 20% constituted the "validation cohort" to test the finally selected model. Fifteen ML models were optimized and evaluated by tenfold cross-validation on the "derivation cohort," and were filtered by the mean absolute error (MAE). On the whole, the nonlinear models outperformed the linear models. The extra-trees' regression algorithm delivered good performance, and was chosen to establish the predictive model. The important features were then analyzed and parameters of the model adjusted to develop the best prediction model, which accurately described the C/D ratio of LTG, especially in the intermediate-to-high range (≥ 22.1 μg mL
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